{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "975284dc",
   "metadata": {},
   "source": [
    "### KNN应用案例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d80817f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier  #导入分类器\n",
    "from sklearn import datasets #导入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d73572d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#加载数据\n",
    "# X是特征数据，y是目标值\n",
    "# X--->y\n",
    "# 特征，进行类别划分\n",
    "# 男生，女生，特征进行划分【泰国人妖】\n",
    "# 大部分情况，都是根据特征进行划分\n",
    "X,y = datasets.load_iris(return_X_y=True) # 鸢尾花，分三类，鸢尾花花萼和花瓣长宽\n",
    "display(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06766e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6728da9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(30, 4)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据拆分\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.2) # 30个测试数据, 120个训练数据\n",
    "display(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e2253a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建模\n",
    "knn = KNeighborsClassifier(n_neighbors=5) # 5个邻居，决定，类别是哪一类，投票\n",
    "knn.fit(X_train, y_train) #训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "698e7db7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 0, 2, 2, 0, 0, 1, 0, 1, 0, 1, 2, 1, 2, 2, 1, 1, 1, 0, 1,\n",
       "       2, 2, 2, 2, 0, 0, 1, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#应用\n",
    "knn.predict(X_test)  #预测测试集，得到测试结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6571abb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 0, 2, 2, 0, 0, 1, 0, 1, 0, 1, 2, 1, 2, 2, 1, 1, 1, 0, 1,\n",
       "       2, 1, 2, 2, 0, 0, 1, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test #真实数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b637684a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#准确率\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f72ad695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b258a12d90>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不同的K值对准确率的影响\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "knn = KNeighborsClassifier()\n",
    "scores = []\n",
    "for k in range(2,50):\n",
    "    knn.set_params(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    score = knn.score(X_test, y_test)\n",
    "    scores.append(score)\n",
    "plt.plot(np.arange(2,50), scores, '*r-')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d5ad68",
   "metadata": {},
   "source": [
    "### 手写数字识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd92ab63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 28, 28)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#有0到9数字图片各500个，0:0~499 1:500~999 ... 9: 4500~4999\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "#1.数据加载\n",
    "data = np.load('./digit.npy')\n",
    "# 5000个手写数字\n",
    "# 每个手写数字：高度是28像素，宽度是28像素\n",
    "# 0~499数字0\n",
    "# 500~999数字1\n",
    "# 最后500个是数字9\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a1447fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "224\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b2617b4430>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.random.randint(0,5000, size=1)[0]\n",
    "print(index)\n",
    "plt.figure(figsize=(2,2))\n",
    "plt.imshow(data[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1972132b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 9, 9, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建目标值\n",
    "y = np.array([0,1,2,3,4,5,6,7,8,9]*500)\n",
    "y = np.sort(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4729ed6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 784)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 强调：算法接收的数据，必须是二维的！！！\n",
    "# 第一维是，样本数量，第二维是每个样本的特征！\n",
    "data = data.reshape(5000,-1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e19e7b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4500, 784)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(500, 784)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#拆分数据，90%做为训练数据，10%做为测试数据(验证数据)\n",
    "X_train,X_test,y_train,y_test = train_test_split(data, y, test_size=0.1) #保留 500个数据作为验证数据\n",
    "display(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "511de493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=10)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建模型\n",
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5d74ea19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 7, 6, 2, 7, 7, 2, 8, 1, 6, 4, 6, 8, 6, 5, 0, 4, 4, 0, 8])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([7, 2, 5, 7, 7, 7, 2, 8, 4, 6, 4, 6, 8, 6, 5, 0, 4, 4, 0, 8])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.936"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#算法验证\n",
    "y_ = knn.predict(X_test)\n",
    "display(y_[:20], y_test[:20])\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27febea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x3000 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化 50个\n",
    "plt.figure(figsize=(2*5, 3*10))\n",
    "for i in range(50):\n",
    "    #10行50列\n",
    "    plt.subplot(10,5,i+1)\n",
    "    plt.imshow(X_test[i].reshape(28,28))\n",
    "    plt.axis('off') #不显示刻度\n",
    "    plt.title('True:%d\\nPredict:%d' %(y_test[i],y_[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7caa9f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "255.995px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
